The surge in Internet of Things deployments and edge computing infrastructures has heightened the need for ultra low latency, high reliability decision-making systems. This paper presents RDERP: Real Time Decision Making at the Edge Using Quantum-Assisted Reinforcement Learning and Predictive Optimization, a hybrid quantum classical framework designed to enhance adaptability, scalability, processing efficiency in dynamic environments. The proposed system integrates quantum subroutines for accelerated policy evaluation with predictive optimization modules that forecast future system states using time series and probabilistic models. These forecasts enable proactive resource allocation, while quantum acceleration facilitates rapid convergence in large state–action spaces. The framework is evaluated using the Disaster Response Coordination dataset, which models real-time, multi-agent decision-making under uncertainty, along with additional datasets from smart manufacturing, healthcare monitoring, traffic optimization, and drone navigation. Performance is assessed across six key metrics: Decision Velocity, Real-Time Scalability Index, Energy Performance Trade-of Efficiency, Task Completion Assurance Rate, Multi-Agent Coordination Latency, Quantum Computational Gain. Experimental results demonstrate significant performance gains up to 38% reduction in decision latency, 27% faster policy adaptation, and 22% higher energy efficiency over baseline deep reinforcement learning models. Specifically, the DRC dataset achieved the highest DV of 25.9166 decisions/s, RSI of 0.928, and TCAR of 97.4192%, with a MACL of 3.08 ms. Quantum acceleration benefits are evidenced by QCG values ranging from 1.6525 to 1.8209. These results demonstrate that RDERP achieves a robust balance among speed, scalability, energy efficiency, and reliability, rendering it highly suitable for latency-sensitive and resource-constrained edge computing environments.
The surge in Internet of Things deployments and edge computing infrastructures has heightened the need for ultra low latency, high reliability decision-making systems. This paper presents RDERP: Real Time Decision Making at the Edge Using Quantum-Assisted Reinforcement Learning and Predictive Optimization, a hybrid quantum classical framework designed to enhance adaptability, scalability, processing efficiency in dynamic environments. The proposed system integrates quantum subroutines for accelerated policy evaluation with predictive optimization modules that forecast future system states using time series and probabilistic models. These forecasts enable proactive resource allocation, while quantum acceleration facilitates rapid convergence in large state–action spaces. The framework is evaluated using the Disaster Response Coordination dataset, which models real-time, multi-agent decision-making under uncertainty, along with additional datasets from smart manufacturing, healthcare monitoring, traffic optimization, and drone navigation. Performance is assessed across six key metrics: Decision Velocity, Real-Time Scalability Index, Energy Performance Trade-of Efficiency, Task Completion Assurance Rate, Multi-Agent Coordination Latency, Quantum Computational Gain. Experimental results demonstrate significant performance gains up to 38% reduction in decision latency, 27% faster policy adaptation, and 22% higher energy efficiency over baseline deep reinforcement learning models. Specifically, the DRC dataset achieved the highest DV of 25.9166 decisions/s, RSI of 0.928, and TCAR of 97.4192%, with a MACL of 3.08 ms. Quantum acceleration benefits are evidenced by QCG values ranging from 1.6525 to 1.8209. These results demonstrate that RDERP achieves a robust balance among speed, scalability, energy efficiency, and reliability, rendering it highly suitable for latency-sensitive and resource-constrained edge computing environments.
The integration of Quantum-Inspired Reinforcement Learning (QiRL) into Edge-Driven CyberPhysical Systems (CPS) presents a transformative approach to enhance real-time decision-making in distributed environments. While classical reinforcement learning (RL) struggles with scalability and computational complexity in edge-CPS, quantum-inspired Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems algorithms leverage superposition and entanglement principles to optimize policy exploration and convergence. This paper proposes a novel QiRL framework that combines quantum-inspired optimization with deep RL, enabling efficient resource allocation and adaptive control in latency-sensitive applications (e.g., smart grids, autonomous drones). We formalize the mathematical foundations of QiRL, design a hybrid neural-quantum architecture for edge deployment, and evaluate its performance under dynamic CPS constraints. Simulations on IoT-based case studies demonstrate 20−35% faster convergence and 15% higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.
The current natural evolution of the world of real-time decision-making algorithms is unable to keep up with the fast changing complexity and dynamics of today’s world. Particularly in automotive, smart grids, robotics, or other applying fields, such ultra efficient decision making is demanded in a situation of rapidly changing conditions with high requirements on computation resources. As the first contribution, Quantum Inspired Reinforcement Learning (QIRLF) proposes a new method of addressing these challenges via marrying the fundamental elements of quantum theory to reinforcement learning. At the same time, QIRLF exploits quantum inspired state representation to explore many potential states concomitantly, thereby improving the efficiency in its decisions making process. Besides quantum tunneling and probabilistic action choice mechanisms faster convergence to optimal solutions are possible in values of state spaces, and multi-agent entanglement improves cooperation between the agents. The framework is adaptable in being able to make real time decisions in dynamic environments and is efficient by having quantum feedback loops that continuously refine decision policies. QIRLF reduces significantly the decision-making time and computational load compared to traditional, by using quantum inspired techniques. By applying this approach we not only enhance decision speed and reliability but also help in developing effective, energy efficient solutions for the complex real time systems, making it possible for more advanced, environment friendly technologies in systems needing fast, accurate and quick decisions.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
The rapid expansion of large-scale data analytics (LSDA) has presented significant issues concerning computing efficiency, scalability, adaptive learning, and model interpretability. In recent years, quantum-inspired deep reinforcement learning (QIDRL) has emerged as a promising research paradigm that integrates quantum-inspired optimization (QIO) concepts with deep reinforcement learning (DRL) approaches to enhance intelligent decision-making and complicated data processing. The need for transparent and reliable artificial intelligence (AI) systems has heightened the significance of interpretable learning frameworks that can elucidate model behavior and prediction results. This work offers a thorough examination of interpretable QIDRL methods for extensive data analysis. The review methodically examines current advancements in QIO models, interpretable RL architectures, policy explanation mechanisms, and scalable deep learning (DL) frameworks used for high-dimensional and dynamic datasets. The report examines the use of these methodologies across many sectors, including healthcare analytics, cybersecurity, smart transportation, industrial automation, financial forecasting, and intelligent Internet of Things (IoT) ecosystems. The study examines the benefits of QIDRL techniques in improving convergence speed, adaptive decision-making, feature optimization, and interpretability inside extensive analytical systems. Furthermore, significant research obstacles like computational complexity, training instability, scalability limitations, insufficient benchmark datasets, and the absence of defined criteria for interpretability assessment are thoroughly examined. The study emphasizes new research trends and prospects for creating robust, efficient, and transparent QIDRL frameworks for advanced intelligent DA applications.
The increasing demands for ultra-reliable low-latency communication (URLLC), high-capacity, connectivity, and mobility in next-generation wireless networks have driven the integration of unmanned aerial vehicles (UAVs) as flexible and adaptive communication nodes. UAV-assisted networks offer enhanced coverage, dynamic deployment, and cost-effective infrastructure for the sixth generation (6G) and beyond. However, optimizing UAV trajectory planning, resource allocation, and interference management is particularly challenging, especially in dynamic and large-scale environments. Classical reinforcement learning (RL) techniques, while effective in adaptive optimization, suffer from computational inefficiencies and slow convergence in high-dimensional state-action spaces. To address these constraints, quantum reinforcement learning (QRL) emerges as a novel paradigm that leverages quantum computing principles, such as superposition and entanglement, to enhance decision-making efficiency. This study provides a comprehensive overview of QRL algorithms and investigates their transformative potential in UAV-assisted wireless communication networks. We analyze UAV deployment strategies, network architectures, and topologies, highlighting their advantages as well as inherent challenges. Furthermore, we examine the limitations of classical RL algorithms and assess how QRL overcomes them while improving computational efficiency and exploration capabilities. In addition, we present a detailed review of existing QRL algorithms applicable to next-generation UAV-enhanced wireless networks, identifying key challenges that must be addressed for practical deployment. Finally, we identify open research challenges and offer future directions for integrating QRL into UAV-assisted communication, paving the way for scalable, intelligent, and high-performance wireless systems.
This work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensuring convergence to optimal policies within complex operational environments. The proposed quantum-aided DRL framework that operates in a military surveillance system exploits quantum computing’s parallel processing capabilities to encode operational states and actions into quantum states, significantly reducing the dimensionality of the decision space. For the very first time in literature, we propose a quantum-enhanced actor-critic method, utilizing quantum circuits for policy representation and optimization. Through comprehensive simulations, we demonstrate that our framework improves DoA estimation accuracy by 91.66% and 82.61% over existing DRL algorithms with faster convergence rate, and effectively manages the trade-off between sensing and communication and optimizing task offloading decisions under stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively.